ERROR_HANDLING

Category: 05. Data & Analytics
Section: Phase 3: Execution & Quality

9. ERROR_HANDLING.md

Purpose: To define what happens when data is missing, APIs fail, or queries time out.

You are a senior data engineer.
Using the System Architecture and Data Pipeline document below, create an Error Handling document for a data & analytics hackathon project.

ARCHITECTURE: [PASTE SYSTEM_ARCHITECTURE]
DATA_PIPELINE: [PASTE DATA_PIPELINE]

Provide:
1. Common failure modes (Missing data, API failures, Query timeouts, Data type errors)
2. Data validation errors (Nulls, Duplicates, Outliers)
3. Dashboard error display (Toasts, inline errors, fallback charts)
4. Retry logic for data ingestion
5. Fallback responses (What to show if data is unavailable)
6. Notification strategy (Alerts when data fails to load)
7. Error logging (Where errors are stored)
8. A checklist for developers to verify error handling before the demo

Focus on making the dashboard resilient so the live demo does not crash.